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This repository is for PointASNL introduced in the following paper

Xu Yan, Chaoda Zheng, Zhen Li*, Sheng Wang and Shuguang Cui, "PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling", CVPR 2020 [arxiv].

If you find our work useful in your research, please consider citing:

  title={Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling},
  author={Yan, Xu and Zheng, Chaoda and Li, Zhen and Wang, Sheng and Cui, Shuguang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},

Getting Started

(1) Set up

Clone the repository:

git clone

Installation instructions for Ubuntu 16.04 (available at CUDA10):

  • Make sure CUDA and cuDNN are installed. Only this configurations has been tested:

    • Python 3.6.9, TensorFlow 1.13.1, CUDA 10.1
  • Follow Tensorflow installation procedure.

  • Compile the customized Tensorflow operators by sh N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts

(2) ModelNet40 Classification

Aligned ModelNet40 dataset can be found here. Since the randomness of data augmentation, the result of this code maybe slightly different from the result in paper, but it should be around 93%.

Data without Noise

It will cost relatively long time in first epoch for cache construction.

# Training 
$ python --data [MODELNET40 PATH] --exp_dir PointASNL_without_noise

# Evaluation 
$ python --data [MODELNET40 PATH] --model_path log/PointASNL_without_noise/best_model.ckpt

Data with Noise

Model with AS module is extremely robust for noisy data. You can use adaptive sampling by setting --AS .

# Training 
$ python --data [MODELNET40 PATH] --exp_dir PointASNL_with_noise --AS

# Evaluation on noisy data 
$ python --data [MODELNET40 PATH]  --model_path log/PointASNL_with_noise/best_model.ckpt --AS --noise

(3) ScanNet Segmentation

We provide two options for training on ScanNet dataset (with or without pre/post processing). With grid sampling processing, more input points and deeper network structure, our PointASNL can achieve 66.6% on ScanNet benchmark.

Data Preparation

Official ScanNet dataset can be downloaded here. If you choose training without grid sampling, you need firstly run ScanNet/, otherwise you can skip to training step.

Data without Processing

This method converges relatively slower, and will achieve result around 63%.

# Training 
$ cd ScanNet/
$ python --data [SCANNET PATH] --log_dir PointASNL

# Evaluation 
$ cd ScanNet/
$ python --data [SCANNET PATH]  --model_path log/PointASNL/latest_model.ckpt 

Data with Grid Sampling

We highly recommend training with this method, although it takes a long time to process the raw data, it can achieve results around 66% and will be faster to converge. Grid sampling pre-processing will be automatically conducted before training.

# Training 
$ cd ScanNet/
$ python --data [SCANNET PATH] --log_dir PointASNL_grid --num_point 10240 --model pointasnl_sem_seg_res --in_radius 2

# Evaluation 
$ cd ScanNet/
$ python --data [SCANNET PATH]  --model_path log/PointASNL_grid/latest_model.ckpt 

Pre-trained Model

Model mIoU Download
pointasnl_sem_seg_res 66.93 ckpt-163.9M

(4) SemanticKITTI Segmentation

  • SemanticKITTI dataset can be found here. Download the files related to semantic segmentation and extract everything into the same folder.
  • We add codes with grid sampling processing, which can achieve better result of around 52% (using --prepare_data just in the first running).
  • Please using official semantic_kitti_api for evaluation.
# Training 
$ cd SemanticKITTI/
$ python --data [SemanticKITTI PATH] --log_dir PointASNL --with_remission
# or
$ python --data [SemanticKITTI PATH] --log_dir PointASNL_grid --prepare_data 

# Evaluation 
$ cd SemanticKITTI/
$ python --data [SemanticKITTI PATH]  --model_path log/PointASNL/latest_model.ckpt  --with_remission
# or
$ python --data [SemanticKITTI PATH] --model_path log/PointASNL_grid/best_model.ckpt --test_area [e.g., 08]



This repository is released under MIT License (see LICENSE file for details).


PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling (CVPR 2020)








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